CN101577011B - Method and device for selecting characteristics of three-dimensional objects - Google Patents
Method and device for selecting characteristics of three-dimensional objects Download PDFInfo
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Abstract
The invention discloses a method and a device for selecting characteristics of three-dimensional objects, belonging to the field of multimedia technology. The method comprises the steps of: obtaining similarity sets of the three-dimensional objects in the condition of each characteristic; selecting a least-similarity set; combining the characteristic corresponding to the least-similarity set with other characteristics, obtaining new combined characteristics, obtaining a similarity set of the three-dimensional objects in the condition of the new combined characteristics till obtaining a similarity set which is an empty set, and taking the new combined characteristic corresponding to the similarity set which is an empty set as an optimum similarity characteristic of the three-dimensional objects. By obtaining the similarity sets of the three-dimensional objects in the condition of each characteristic, the method and the device obtain the optimum similarity characteristic of the three-dimensional objects, reduce the wok load during characteristic selection and facilitate the marking, identification, retrieval and other operations conducted on the three-dimensional objects.
Description
Technical field
The present invention relates to multimedia technology field, particularly a kind of feature selection approach and device of the three dimensional object based on many views.
Background technology
In recent years, along with the image-based three dimensional object represents development with Rendering, become gradually focus based on technology such as the mark of the three dimensional object of many views, identification, retrievals.In these technology, the view of three dimensional object often represents with one group or several groups of bottom visual signatures, and the bottom visual signature is mainly the feature of Pixel-level, comprises color, texture, shape etc.The importance of these features when describing different classes of object is different, and role is usually also different when comparing two class objects, so need to select feature.
Having now in based on technology such as the mark of the three dimensional object of many views, identification, retrievals, is usually the mode by artificial selection, realizes the feature selecting of three dimensional object.
In realizing process of the present invention, the inventor finds that there is following shortcoming in prior art:
If the feature of three dimensional object is more, or when in database, the number of three dimensional object is huger, when carrying out feature selecting by the user, it is quite heavy that workload will become.
Summary of the invention
In order to reduce the workload when selecting the feature of three dimensional object, be convenient to three dimensional object mark, identify, the operation such as retrieval, the embodiment of the present invention provides a kind of feature selection approach and device of the three dimensional object based on many views.Described technical scheme is as follows:
On the one hand, provide a kind of feature selection approach of the three dimensional object based on many views, in advance the three dimensional object in the three dimensional object database manually has been divided into several classifications, and each classification is marked; Each three dimensional object is placed in video camera array takes, obtain two dimension view set corresponding to each three dimensional object, described method comprises:
Under every kind of feature of the three dimensional object that extracts, the similarity of every width view of the described three dimensional object of calculating and all views of other classification three dimensional objects;
According to the similarity that calculates, for every width view of described three dimensional object is selected the most similar view, and search the classification under the most similar view, obtain the Similarity Class set of described three dimensional object under every kind of feature;
Select minimum Similarity Class set, obtain described minimum Similarity Class set characteristic of correspondence;
With described minimum Similarity Class set characteristic of correspondence respectively with other Feature Combinations, obtain new assemblage characteristic, and obtain the Similarity Class set of described three dimensional object under described new assemblage characteristic, until obtain Similarity Class set into empty set, with described be that new assemblage characteristic corresponding to the Similarity Class set of empty set is as the near-optimization feature of described three dimensional object.
On the other hand, provide a kind of feature selecting device of the three dimensional object based on many views, in advance the three dimensional object in the three dimensional object database manually has been divided into several classifications, and each classification is marked; Each three dimensional object is placed in video camera array takes, obtain two dimension view set corresponding to each three dimensional object, described device comprises:
Acquisition module is used for obtaining respectively the Similarity Class set of three dimensional object under every kind of feature;
Select module, be used for selecting minimum Similarity Class set, obtain described minimum Similarity Class set characteristic of correspondence;
Processing module, be used for described minimum Similarity Class set characteristic of correspondence respectively with other Feature Combinations, obtain new assemblage characteristic, and obtain the Similarity Class set of described three dimensional object under described new assemblage characteristic, until obtain Similarity Class set into empty set, with described be that new assemblage characteristic corresponding to the Similarity Class set of empty set is as the near-optimization feature of described three dimensional object;
Described acquisition module comprises:
Extraction unit is for every kind of feature extracting described three dimensional object;
Computing unit is used under every kind of feature of the described three dimensional object that extracts, the similarity of every width view of the described three dimensional object of calculating and all views of other classification three dimensional objects;
Selected cell for the similarity that calculates according to described computing unit, is that every width view of described three dimensional object is selected the most similar view;
Acquiring unit is used for searching the affiliated classification of the most similar view that selected cell is selected, and obtains the Similarity Class set of described three dimensional object under every kind of feature.
The beneficial effect of the technical scheme that the embodiment of the present invention provides is:
By taking full advantage of the different characteristic of three dimensional object, obtain the Similarity Class set of three dimensional object under every kind of feature, thereby obtain this three dimensional object to be different from the near-optimization feature of other classification three dimensional objects, when avoiding the feature redundancy, workload when also having reduced feature selecting, not only be convenient to three dimensional object mark, identify, the operation such as retrieval, simultaneously, the method that the embodiment of the present invention provides is also applicable to larger database.
Description of drawings
In order to be illustrated more clearly in the technical scheme in the embodiment of the present invention, during the below will describe embodiment, the accompanying drawing of required use is done to introduce simply, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain according to these accompanying drawings other accompanying drawing.
Fig. 1 is the feature selection approach process flow diagram based on the three dimensional object of many views that the embodiment of the present invention 1 provides;
Fig. 2 is the feature selection approach process flow diagram based on the three dimensional object of many views that the embodiment of the present invention 2 provides;
Fig. 3 is the feature selecting apparatus structure schematic diagram based on the three dimensional object of many views that the embodiment of the present invention 3 provides;
Fig. 4 be the embodiment of the present invention 3 provide based on the acquisition module structural representation in the feature selecting device of the three dimensional object of many views.
Embodiment
For making the purpose, technical solutions and advantages of the present invention clearer, embodiment of the present invention is described further in detail below in conjunction with accompanying drawing.
Embodiment 1
Referring to Fig. 1, the present embodiment provides a kind of feature selection approach of the three dimensional object based on many views, and the method idiographic flow is as follows:
101: obtain respectively the Similarity Class set of three dimensional object under every kind of feature;
102: select minimum Similarity Class set;
103: with described minimum Similarity Class set characteristic of correspondence respectively with other Feature Combinations, obtain new assemblage characteristic, and obtain the Similarity Class set of described three dimensional object under described new assemblage characteristic, until obtain Similarity Class set into empty set, with described be that new assemblage characteristic corresponding to the Similarity Class set of empty set is as the near-optimization feature of described three dimensional object.
The method that the present embodiment provides, by taking full advantage of the different characteristic of three dimensional object, obtain the Similarity Class set of three dimensional object under every kind of feature, thereby obtain this three dimensional object to be different from the near-optimization feature of other classification three dimensional objects, workload when not only having reduced feature selecting has been avoided the feature redundancy, be convenient to three dimensional object mark, identify, the operation such as retrieval, simultaneously, also applicable to larger database.
Embodiment 2
The present embodiment provides a kind of feature selection approach of the three dimensional object based on many views, before the feature selecting of carrying out three dimensional object, first all three dimensional objects in the three dimensional object database manually is divided into several classifications, and each classification is marked; Secondly, each three dimensional object is placed in video camera array takes, obtain two dimension view set corresponding to each three dimensional object, these two dimension view set have consisted of the three dimensional object database based on many views.The prerequisite of the method that enforcement the present embodiment provides is: after feature extraction having been carried out in the view set, if used whole features of three dimensional object, each three dimensional object should distinguish over and own other different classes of three dimensional objects, if can not reach the effect of differentiation, the classification that needs to increase the feature of three dimensional object or change three dimensional object marks, until satisfy this prerequisite.
For convenience of explanation, so that the three dimensional object S in the three dimensional object database is carried out feature selecting as example, first this three dimensional object S is placed in video camera array and takes, obtain describing the two dimension view set X={x of this three dimensional object S
1, x
2..., x
n, every width view all can be extracted N kind feature, and referring to Fig. 2, the method flow that the present embodiment provides is as follows:
201: extract arbitrary feature of three dimensional object S, and calculate under this arbitrary feature the similarity of every width view of three dimensional object S and all views of other classification three dimensional objects;
For this step, the view of three dimensional object often represents with one or more groups bottom visual signature, and the bottom visual signature is mainly the feature of Pixel-level, includes but not limited to color, texture, shape etc.Wherein, each feature has again multiple extracting mode, and the present embodiment does not limit the concrete extracting mode of feature.After extracting arbitrary feature of three dimensional object S, the every width view that obtains describing three dimensional object S is to proper vector that should arbitrary feature, according to the proper vector that obtains, calculate under this arbitrary feature the similarity of every width view of three dimensional object S and all views of other classification three dimensional objects.
Particularly, when calculating similarity, have multiple account form, the present embodiment does not limit the concrete mode of calculating similarity, only take adopt Euclidean distance as similarity as example, for example, arbitrary F that is characterized as of extraction
j, obtain view and concentrate every width view at this feature F
jUnder proper vector, calculate the Euclidean distance of all views of other classification three dimensional objects in every width view and database, with the Euclidean distance that calculates as similarity.Wherein, Euclidean distance is used for calculating the non-similarity of two width views, and the Euclidean distance of two width views is less, illustrates that the similarity of two width views is higher.For two dimension view, the quadratic sum that the calculating of Euclidean distance is two width view variate-values is square root again.
202: according to the similarity that calculates, for every width view of three dimensional object S is selected the most similar view, and search the classification under the most similar view, obtain the Similarity Class set of three dimensional object S under this arbitrary feature;
Wherein, because the Euclidean distance of two width views is less, illustrate that two width views are more similar, therefore, for this step, according to the similarity that has calculated, namely can be every width view and select the most similar view.
203: repeating step 201 and 202 obtains the Similarity Class set of three dimensional object S under all features;
Particularly, three dimensional object S all can obtain the Similarity Class set of a correspondence under each feature, and therefore, how many features three dimensional object S has, and can obtain what corresponding Similarity Class set.
204: select the minimum Similarity Class set of three dimensional object S, obtain this minimum Similarity Class set characteristic of correspondence;
Particularly, because choosing minimum Similarity Class set, in order to realize three dimensional object S is distinguished over the three dimensional object of other classifications in the three dimensional object database, therefore, for this step, if there are two or more equal minimum Similarity Class set, can be according to the principle of choosing at random, select the minimum Similarity Class set of three dimensional object S, the present embodiment does not specifically limit the selection principle of minimum Similarity Class set.
205: with minimum Similarity Class set characteristic of correspondence respectively with other Feature Combinations, obtain new assemblage characteristic, and the Similarity Class set of Calculation of Three Dimensional object S under this new assemblage characteristic, be empty set if there is the Similarity Class set under certain new assemblage characteristic, this new assemblage characteristic is the near-optimization feature of three dimensional object S, and the former feature that this new assemblage characteristic is corresponding can consist of the near-optimization characteristic set of three dimensional object S.
Particularly, so that 5 classifications to be arranged in the three dimensional object database, be designated as C={C
1, C
2, L, C
5Be example, establishing the affiliated classification of three dimensional object S is C
1, the minimum Similarity Class set characteristic of correspondence of three dimensional object S is F
j, this feature F
jWith another feature F
kObtain new assemblage characteristic after combination.
If establish feature F
jCorresponding minimum Similarity Class set is C
s={ C
s2, wherein, C
s2Corresponding C
2Feature F
kCorresponding minimum Similarity Class set is C
s={ C
s2, C
s3, wherein, C
s3Corresponding C
3, feature F
jWith F
kSimilarity Class set corresponding to new assemblage characteristic after combination is C
s={ C
s2, this set is nonvoid set, characterization F
jWith F
kNew assemblage characteristic after combination is not the best fit approximation feature;
If establish feature F
jCorresponding minimum Similarity Class set is C
s={ C
s2, wherein, C
s2Corresponding C
2Feature F
kCorresponding minimum Similarity Class set is C
s={ C
s3, wherein, C
s3Corresponding C
3, feature F
jWith F
kSimilarity Class set corresponding to new assemblage characteristic after combination is empty set, characterization F
jWith F
kNew assemblage characteristic after combination is the best fit approximation feature.
Assemblage characteristic is asked the Similarity Class set, be namely every kind of feature that assemblage characteristic is corresponding Similarity Class set phase with.
To sum up, to step 205, can obtain the near-optimization feature of three dimensional object S by step 201, the near-optimization feature has been arranged, three dimensional object S can be distinguished over the three dimensional object of other classifications.In like manner, profit uses the same method, and can also try to achieve the near-optimization feature of other three dimensional objects in the three dimensional object database, thereby obtains the near-optimization feature of all three dimensional objects in the three dimensional object database.The near-optimization feature is to describe the best feature of three dimensional object, and it can distinguish each three dimensional object, has represented the characteristic attribute of three dimensional object.Experimental results show that, the near-optimization characteristic set that the method that provides by the present embodiment obtains is not only simplified, and the size of set is relevant with intrinsic dimensionality, in intrinsic dimensionality higher (as 100 to 256 dimensions), during feature more (as 20 left and right), use the near-optimization characteristic set that comprises 5 to 7 features the three dimensional object of three dimensional object and other classifications can be differentiated.
Below, so that M classification to be arranged in the three dimensional object database, be designated as C={C
1, C
2, L, C
MBe example, the method that the present embodiment is provided is illustrated:
Two dimension view set X={x for three dimensional object S
1, x
2..., x
n, extract feature F
jObtain two dimension view and concentrate the proper vector of every width view, calculate the Euclidean distance of all views of every width view and other classification three dimensional objects, choose the view of Euclidean distance minimum as the most similar view, and obtain the classification of similar view, with three dimensional object S at bottom visual signature F
jUnder the Similarity Class set be designated as
At bottom visual signature F
jUnder, the differentiation vector that three dimensional object S distinguishes other classification three dimensional objects is:
A
j={ a
1, a
2..., a
M, wherein,
Wherein, distinguish vectorial A
jRepresented that namely three dimensional object S is at feature F
jUnder the Similarity Class set, and distinguish classification under the 1 the most similar view of correspondence in vector, in like manner, try to achieve the feature differentiation vector set of three dimensional object S under all features and be combined into:
{ A
1, A
2..., A
N, wherein, A
1﹠amp; A
2﹠amp; ... ﹠amp; A
N=0.
The feature differentiation vector that this three dimensional object S tries to achieve under all features has represented the property distinguished (prerequisite of the method that satisfied enforcement the present embodiment provides) of this three dimensional object S and other classifications.
Next, by the near-optimization feature of greedy algorithm Calculation of Three Dimensional object S, specific algorithm is as follows:
1) obtain differentiation vector set A={A
1, A
2..., A
NIn contain the minimum differentiation vector of 1 number as first alternative vector (being minimum Similarity Class set), with A
pBe example as containing the minimum differentiation vector of 1 number.
2) all that distinguish in vectorial set A are distinguished vector, with each distinguish vector and first alternative vector with, result is vectorial as alternative differentiations of upgrading, and the differentiation that obtains upgrading is vectorial gathers, and is designated as A
*(being new Similarity Class set corresponding to assemblage characteristic) is if at A
*In appear as the differentiation vector of full 0, be the near-optimization feature of three dimensional object S for the assemblage characteristic corresponding to differentiation vector of full 0, the former near-optimization characteristic set that is characterized as three dimensional object S of this assemblage characteristic, otherwise, use A
*Turn step 1).
4) obtain the near-optimization characteristic set of all three dimensional objects.
In sum, the method that the present embodiment provides, by taking full advantage of the different characteristic of three dimensional object, obtain the Similarity Class set of three dimensional object under every kind of feature, the near-optimization feature of the three dimensional object that not only can obtain simplifying has been avoided the feature redundancy, is applicable to larger database, workload when also having reduced feature selecting, be convenient to three dimensional object mark, identify, the operation such as retrieval.
Embodiment 3
Referring to Fig. 3, the present embodiment provides a kind of feature selecting device of the three dimensional object based on many views, and described device comprises:
Further, referring to Fig. 4, above-mentioned acquisition module 301 comprises:
Acquiring unit 301d is used for searching the affiliated classification of the most similar view that selected cell 301c selects, and obtains the Similarity Class set of three dimensional object under every kind of feature.
Wherein, computing unit 301b, concrete being used for obtained the proper vector of every width view of three dimensional object under every kind of feature of the three dimensional object that extracts; According to proper vector, the Euclidean distance of every width view of Calculation of Three Dimensional object and all views of other classification three dimensional objects is with the Euclidean distance that the calculates similarity as all views of every width view of three dimensional object and other classification three dimensional objects;
Correspondingly, selected cell 301c specifically is used to every width view of three dimensional object, selects the most similar view of view conduct with its Euclidean distance minimum.
Wherein, because the Euclidean distance of two width views is less, illustrate that two width views are more similar, therefore, according to the similarity that has calculated, namely can be every width view and select the most similar view.
Further, select module 302 when selecting minimum Similarity Class set, if there are a plurality of equal minimum Similarity Class set, select at random a minimum Similarity Class set.
Because choosing minimum Similarity Class set, in order to realize three dimensional object S is distinguished over the three dimensional object of other classifications in the three dimensional object database, therefore, for selecting module 302, if there are two or more equal minimum Similarity Class set, can select the minimum Similarity Class set of three dimensional object S according to the principle of choosing at random, the present embodiment is not to selecting the selection principle of module 302 when selecting minimum Similarity Class set specifically to limit.
In sum, the device that the present embodiment provides, by taking full advantage of the different characteristic of three dimensional object, obtain the Similarity Class set of three dimensional object under every kind of feature, the near-optimization feature of the three dimensional object that not only can obtain simplifying has been avoided the feature redundancy, is applicable to larger database, workload when also having reduced feature selecting, be convenient to three dimensional object mark, identify, the operation such as retrieval.
The invention described above embodiment sequence number does not represent the quality of embodiment just to description.
Part steps in the embodiment of the present invention can utilize software to realize, corresponding software program can be stored in the storage medium that can read, as CD or hard disk etc.
The above is only preferred embodiment of the present invention, and is in order to limit the present invention, within the spirit and principles in the present invention not all, any modification of doing, is equal to replacement, improvement etc., within all should being included in protection scope of the present invention.
Claims (6)
1. the feature selection approach based on the three dimensional object of many views, is characterized in that, in advance the three dimensional object in the three dimensional object database manually is divided into several classifications, and each classification is marked; Each three dimensional object is placed in video camera array takes, obtain two dimension view set corresponding to each three dimensional object, described method comprises:
Under every kind of feature of the three dimensional object that extracts, the similarity of every width view of the described three dimensional object of calculating and all views of other classification three dimensional objects;
According to the similarity that calculates, for every width view of described three dimensional object is selected the most similar view, and search the classification under the most similar view, obtain the Similarity Class set of described three dimensional object under every kind of feature;
Select minimum Similarity Class set, obtain described minimum Similarity Class set characteristic of correspondence;
With described minimum Similarity Class set characteristic of correspondence respectively with other Feature Combinations, obtain new assemblage characteristic, and obtain the Similarity Class set of described three dimensional object under described new assemblage characteristic, until obtain Similarity Class set into empty set, with described be that new assemblage characteristic corresponding to the Similarity Class set of empty set is as the near-optimization feature of described three dimensional object.
2. method according to claim 1, is characterized in that, under every kind of feature of described described three dimensional object extracting, the similarity of every width view of the described three dimensional object of calculating and all views of other classification three dimensional objects specifically comprises:
Under every kind of feature of the described three dimensional object that extracts, obtain the proper vector of every width view of described three dimensional object;
According to described proper vector, calculate the Euclidean distance of all views of every width view of described three dimensional object and other classification three dimensional objects, with the Euclidean distance that the calculates similarity as all views of every width view of described three dimensional object and other classification three dimensional objects;
Correspondingly, the similarity that described basis calculates is selected the most similar view for every width view of described three dimensional object, specifically comprises:
Be every width view of described three dimensional object, select the most similar view of view conduct to its Euclidean distance minimum.
3. method according to claim 1, is characterized in that, during the minimum Similarity Class set of described selection, if there are a plurality of equal minimum Similarity Class set, selects at random a minimum Similarity Class set.
4. the feature selecting device based on the three dimensional object of many views, is characterized in that, in advance the three dimensional object in the three dimensional object database manually is divided into several classifications, and each classification is marked; Each three dimensional object is placed in video camera array takes, obtain two dimension view set corresponding to each three dimensional object, described device comprises:
Acquisition module is used for obtaining respectively the Similarity Class set of three dimensional object under every kind of feature;
Select module, be used for selecting minimum Similarity Class set, obtain described minimum Similarity Class set characteristic of correspondence;
Processing module, be used for described minimum Similarity Class set characteristic of correspondence respectively with other Feature Combinations, obtain new assemblage characteristic, and obtain the Similarity Class set of described three dimensional object under described new assemblage characteristic, until obtain Similarity Class set into empty set, with described be that new assemblage characteristic corresponding to the Similarity Class set of empty set is as the near-optimization feature of described three dimensional object;
Described acquisition module comprises:
Extraction unit is for every kind of feature extracting described three dimensional object;
Computing unit is used under every kind of feature of the described three dimensional object that extracts, the similarity of every width view of the described three dimensional object of calculating and all views of other classification three dimensional objects;
Selected cell for the similarity that calculates according to described computing unit, is that every width view of described three dimensional object is selected the most similar view;
Acquiring unit is used for searching the affiliated classification of the most similar view that selected cell is selected, and obtains the Similarity Class set of described three dimensional object under every kind of feature.
5. device according to claim 4, is characterized in that, described computing unit, and concrete being used for obtained the proper vector of every width view of described three dimensional object under every kind of feature of the described three dimensional object that extracts; According to described proper vector, calculate the Euclidean distance of all views of every width view of described three dimensional object and other classification three dimensional objects, with the Euclidean distance that the calculates similarity as all views of every width view of described three dimensional object and other classification three dimensional objects;
Correspondingly, described selected cell specifically is used to every width view of described three dimensional object, selects the most similar view of view conduct with its Euclidean distance minimum.
6. device according to claim 4, is characterized in that, described selection module if there are a plurality of equal minimum Similarity Class set, is selected a minimum Similarity Class set at random when selecting minimum Similarity Class set.
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| CN100346357C (en) * | 2006-01-19 | 2007-10-31 | 上海交通大学 | Method for directly performing three-dimensional model transformation with three-dimensional bench marks |
| US7348997B1 (en) * | 2004-07-21 | 2008-03-25 | United States Of America As Represented By The Secretary Of The Navy | Object selection in a computer-generated 3D environment |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1860522A (en) * | 2003-07-28 | 2006-11-08 | 兰德马克绘图公司 | System and method for real-time co-rendering of multiple attributes |
| US7348997B1 (en) * | 2004-07-21 | 2008-03-25 | United States Of America As Represented By The Secretary Of The Navy | Object selection in a computer-generated 3D environment |
| CN1952883A (en) * | 2005-10-21 | 2007-04-25 | 三星电子株式会社 | Three dimensional graphic user interface, method and apparatus for providing the user interface |
| CN100346357C (en) * | 2006-01-19 | 2007-10-31 | 上海交通大学 | Method for directly performing three-dimensional model transformation with three-dimensional bench marks |
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